What is Real Problem AI?

Real Problem AI is a curated directory of 200+ real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 200+ such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why do B2B marketers running LinkedIn Ads in 2026 still guess which job titles convert?

LinkedIn Campaign Manager shows clicks and form fills but not which of the 47 included job titles actually became opportunities, so marketers run for 6 weeks before realizing Director of Operations was burning 60 percent of budget.

Category: Marketing, Sales & CRM · Trend: LLM · Opportunity score: 8.0 / 10

What is the “Why do B2B marketers running LinkedIn Ads in 2026 still guess which job titles convert?” problem in 2026?

Who has this problem?

Demand gen manager at a B2B SaaS spending $20k to $200k per month on LinkedIn.

Evidence this problem is real

“LinkedIn will not tell me which job title in my audience converted. I am spending $30k a month and basically optimizing blindfolded.”

Sourced from r/PPC threads on LinkedIn Ads attribution opacity.

Existing players in this space

HockeyStack — Strong attribution, no LinkedIn audience-level title breakdown.
Dreamdata — Similar, requires heavy setup.
LinkedIn native — Opaque audience reporting by design.

What existing players are missing

An agent that joins LinkedIn click IDs to closed-won opportunities, normalizes the 200 free-text job titles in your CRM into canonical roles, and tells you which seniority plus function is actually paying back.

How Real Problem AI scores this opportunity

Aggregate score: 8.0 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 8 / 10

How to build a solution: stack hints

LinkedIn Ads API
Salesforce or HubSpot opp API
Claude for title canonicalization
DuckDB analytics